System and method for magnetic resonance fingerprinting at high field strengths

ABSTRACT

A system and method is provided for operating a high-field magnetic resonance (MR) system includes performing a series of data acquisition modules without respiratory gating. Each data acquisition module is formed of a steady-state free precession pulse sequence. Performing the series of data acquisition modules includes varying at least one of an amplitude of an excitation pulse or a repetition time of the steady-state free precession pulse sequence between adjacent data acquisition modules in the series of data acquisition modules to acquire a series of MR data with random or pseudo-random imaging acquisition parameters. The series of MR data is compared to a dictionary of signal evolution profiles to determine a match between the series of MR data with at least one signal evolution profile in the dictionary indicating at least one quantitative parameter in the subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on, claims the benefit of, and incorporatesherein by reference in its entirety U.S. Provisional Patent Application62/167,969, filed May 29, 2015.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under R01CA179956awarded by the National Institutes of Health. The government has certainrights in the invention.

BACKGROUND

The present disclosure relates generally to systems and methods forperforming magnetic resonance (MR) studies. More particularly, thedisclosure relates to systems and methods for performing magneticresonance fingerprinting (MRF) at high field strengths, such as 4.7Tesla (T) and above.

MR studies use the nuclear magnetic resonance (NMR) phenomenon toproduce images. When a substance such as human tissue is subjected to auniform magnetic field, such as the so-called main magnetic field, B₀,of an MRI system, the individual magnetic moments of the nuclei in thetissue attempt to align with this B₀ field, but precess about it inrandom order at their characteristic Larmor frequency, ω. If thesubstance, or tissue, is subjected to a so-called excitationelectromagnetic field, B₁, that is in the plane transverse to the B₀field and that has a frequency near the Larmor frequency, the netaligned magnetic moment, referred to as longitudinal magnetization, maybe rotated, or “tipped,” into the transverse plane to produce a nettransverse magnetic moment, referred to as transverse magnetization. Asignal is emitted by the excited nuclei or “spins” after the excitationfield, B₁, is terminated, and this signal may be received and processedto form an image.

Though the most-common clinical MR systems utilize a static magneticfield strength of 1.5 Tesla (T) or 3.0 Tesla (T), high field,preclinical (≧4.7 T) MRI scanners are also available. In contrast toclinical MRI scanning, high-field preclinical MRI studies are oftenquantitative by nature and may require assessment of multiple imagingparameters during a single scanning session. These quantitativepreclinical MRI studies provide the opportunity to assesspathophysiologic changes associated with disease progression andtherapeutic efficacy. In addition, rigorous validation of these MRIassessments has the potential to inform future clinical imaging studies.Therefore, a significant effort is ongoing to develop robust andeffective acquisition and reconstruction techniques that can be usedroutinely in clinical practice preclinical MRI acquisition andreconstruction techniques.

Conventional MRI quantification methods are typically based on linear ornonlinear curve fitting to various MRI models. The implementation ofthese established model-based methods, such as T₁ and T₂ relaxation timeestimation, are straightforward. However, these conventionalquantification methods are susceptible to multiple sources of errorsincluding cardiac and respiratory motion artifacts, as well as,inhomogeneity in the main magnetic field (B₀). Importantly, thepotential for these errors are significantly increased on high fieldpreclinical MRI scanners, where B₀ inhomogeneities are increased, andother confounding factors can also be present. In addition, temporalerrors can be observed in high-field studies that require multipleimaging parameter estimates (ex. diffusion and perfusion) and extendedor sequential scans. Therefore, new MRI acquisition and reconstructionmethods for preclinical imaging applications are needed that are not assusceptible to these error sources and can readily obtain estimates ofmultiple imaging parameters simultaneously.

SUMMARY

The present disclosure overcomes the aforementioned drawbacks byproviding a system and method for performing quantitative assessmentsusing MR systems at high static magnetic field strengths. A magneticresonance fingerprinting (MRF) process is performed using a steady-statefree precession (SSFP) pulse sequence that is repeated in a series ofacquisition modules using temporally-varying imaging parameters duringfree breathing to acquire the MRF data. The quantitative MRF maps arethen obtained using an MRF matching process.

In accordance with one aspect of the disclosure, a method is providedfor operating a high-field magnetic resonance (MR) system to acquirequantitative data from a subject arranged with in the MR system. Themethod includes controlling the high-field MR system to perform apreparation module configured to perform one of an inversion pulse or amagnetization preparation. The method also includes controlling thehigh-field MR system to perform a series of data acquisition moduleswithout respiratory gating. Each data acquisition module is formed of asteady-state free precession pulse sequence. Performing the series ofdata acquisition modules includes varying at least one of an amplitudeof an excitation pulse or a repetition time of the steady-state freeprecession pulse sequence between adjacent data acquisition modules inthe series of data acquisition modules to acquire a series of MR datawith random or pseudo-random imaging acquisition parameters. The methodalso includes comparing the series of MR data to a dictionary of signalevolution profiles to determine a match between the series of MR datawith at least one signal evolution profile in the dictionary andgenerating a report indicating at least one quantitative parameter inthe subject based on the MR data and the match to the at least onesignal evolution profile in the dictionary.

In accordance with another aspect of the disclosure, a magneticresonance (MR) system is disclosed that includes a magnet systemconfigured to generate a static magnetic field of at least 4.7 Teslaabout at least a portion of a subject arranged in the MR system. The MRsystem also includes a gradient system configured to establish at leastone magnetic gradient field with respect to the static magnetic fieldand a radio frequency (RF) system configured to deliver excitationpulses to the subject and acquire imaging data from the subject. The MRsystem further includes a computer system programmed to control thegradient system and RF system to perform a preparation module includingone of an inversion pulse or a magnetization preparation. The computersystem is also programmed to control the gradient system and RF systemto perform a series of data acquisition modules, wherein each dataacquisition module is formed of a steady-state free precession pulsesequence. Performing the series of data acquisition modules alsoincludes varying at least one of an amplitude of an excitation pulse ora repetition time of the steady-state free precession pulse sequencebetween adjacent data acquisition modules in the series of dataacquisition modules to acquire a series of MR data including respiratoryspikes. The computer system is further programmed to compare the seriesof MR data to a dictionary of signal evolution profiles to determine amatch between the series of MR data with at least one signal evolutionprofile in the dictionary and, based on the match, determine at leastone quantitative parameter of the subject.

In accordance with another aspect of the disclosure, a method isdisclosed for operating a high-field magnetic resonance (MR) system toperform a magnetic resonance fingerprinting process. The method includescontrolling a high-field MR system with a static magnetic field of atleast 4.7 Tesla to perform a preparation module configured to performone of an inversion pulse or a magnetization preparation. The methodalso includes controlling the high-field MR system to, following thepreparation module, perform a series of data acquisition modules withoutrespiratory gating. Each data acquisition module is formed of asteady-state free precession pulse sequence and wherein performing theseries of data acquisition modules includes imaging parameters toacquire a series of MR data. The method also includes performing amagnetic resonance fingerprinting reconstruction process by comparingthe series of MR data to a dictionary to generate a report indicating atleast one quantitative parameter in the subject.

The foregoing and other aspects and advantages of the invention willappear from the following description. In the description, reference ismade to the accompanying drawings which form a part hereof, and in whichthere is shown by way of illustration a preferred embodiment of theinvention. Such embodiment does not necessarily represent the full scopeof the invention, however, and reference is made therefore to the claimsand herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example of a magnetic resonance imaging(MRI) system configured to employ the present disclosure.

FIG. 2A is a graphic representation of an example dynamic MR pulsesequence for directing the MRI system of FIG. 1 to acquire image data inaccordance with the present disclosure.

FIG. 2B is a graph showing an example of one strategy for varying flipangle (FA) degrees in accordance with the present disclosure.

FIG. 2C is a graph showing an example of one strategy for varyingrepetition times in accordance with the present disclosure.

FIG. 3 is an example flow chart setting forth some non-limiting examplesof steps of a method in accordance with the present disclosure.

FIG. 4A is a graph of T1 for data acquired from phantoms using a spinecho (SE) pulse sequence compared with the MRF pulse sequence for usewith high fields in accordance with the present disclosure.

FIG. 4B is a graph of T2 for data acquired from phantoms using a spinecho (SE) pulse sequence compared with the MRF pulse sequence for usewith high fields in accordance with the present disclosure.

FIG. 5 is a graph of MRF signal intensity profiles acquired withoutrespiratory gating across a series of MR data compared againstdictionary data.

DETAILED DESCRIPTION

Over the last few years, a new category of quantification in MRI hasemerged that uses dictionary-based methods to “match” acquired datarather than conventional parameter estimation techniques usingerror-minimization methods. Some of these methods, which leveragecompressed sensing for parameter mapping, have been developed for bothclinical and preclinical applications and have been shown to limitquantification errors and/or reduce the overall time to acquirequantitative data sets. More recently, a magnetic resonancefingerprinting (MRF) has been introduced. MRF uses a distinctacquisition and quantification strategy that combines a prioriacquisition parameter variation with a dictionary-based matchingalgorithm to obtain quantitative assessments of multiple imagingparameters simultaneously. Further description of the fundamentals ofMRF is described in co-pending application Ser. No. 13/051,044, which isincorporated herein by reference in its entirety.

The MRF technique was initially developed for low-field (1.5 T-3 T),clinical MRI scanners and was used to simultaneously generate T₁, T₂,and M₀ maps in healthy human brains. The present disclosure recognizesthat the MRF technique can be leveraged to inherently resist errors dueto motion artifacts as motion or noise is not included/encoded into thetheoretical signal evolution profiles that make up the MRF dictionary.Therefore, MRF provides a basis to generate multi-parametric assessmentsfor high-field imaging applications with limited impact of motionartifacts.

As will be described, the present disclosure provides an effective MRFacquisition and analysis algorithm for high-field, MRI scanners, such asare generally found in only preclinical situations. A priori variation,such as generally random or pseudo-random, in flip angles (FA) andrepetition time (TR) variations can be combined with any dynamic MRacquisition (e.g., steady-state free precession, gradient-recalled echo,True Fast Imaging with Steady-State Free Precession (True FISP), spoiledgradient echo) to simultaneously generate quantitative maps of T₁ and T₂relaxation times or other MR parameters (e.g., perfusion, diffusivity,etc.) from a single scan. Random and pseudo-random imaging parameterscan be selected or determined in any of a variety of manners.

Notably, FISP is but one example of a pulse sequence, as will bedescribed. Other multi-echo, gradient echo sequences include, asnon-limiting examples, GRASS, CE-FAST, SPGR (FLASH), and GRASE.Evaluation of these MRF estimates of T₁, T₂, and M₀, in phantoms incomparison with conventional MRI techniques with much longer acquisitiontimes showed favorable results. In vivo MRF data from healthy brain andkidneys verify the robustness of the MRF technique to respiratory motionartifacts. In vivo MRF data from a glioma model demonstrates thesensitivity of the MRF technique to known pathology as shown byhistology. The impact of RF excitation pulse profile as well as thenumber of acquired MRF images on the T₁, and T₂ estimates have also beendemonstrated. It must also be noted that the MRF methodology can becombined with a flexible preparation module or modules to obtainestimates of virtually any MRI parameter (e.g., perfusion, chemicalexchange saturation transfer, pH, amide proton transfer, etc.).

Referring to FIG. 1, an example of an MRI system 100 is illustrated. TheMRI system 100 includes a workstation 102 having a display 104 and akeyboard 106. The workstation 102 includes a processor 108 that iscommercially available to run a commercially-available operating system.The workstation 102 provides the operator interface that enables scanprescriptions to be entered into the MRI system 100. The workstation 102is coupled to four servers: a pulse sequence server 110; a dataacquisition server 112; a data processing server 114; and a data storeserver 116. The workstation 102 and each server 110, 112, 114, and 116are connected to communicate with each other.

The pulse sequence server 110 functions in response to instructionsdownloaded from the workstation 102 to operate a gradient system 118 anda radiofrequency (RF) system 120. Gradient waveforms necessary toperform the prescribed scan are produced and applied to the gradientsystem 118, which excites gradient coils in an assembly 122 to producethe magnetic field gradients G_(x), G_(y), and G_(z) used for positionencoding MR signals. The gradient coil assembly 122 forms part of amagnet assembly 124 that includes a polarizing magnet 126 and awhole-body RF coil 128 and/or local coil.

RF excitation waveforms are applied to the RF coil 128, or a separatelocal coil, such as a head coil, by the RF system 120 to perform theprescribed magnetic resonance pulse sequence. Responsive MR signalsdetected by the RF coil 128, or a separate local coil, are received bythe RF system 120, amplified, demodulated, filtered, and digitized underdirection of commands produced by the pulse sequence server 110. The RFsystem 120 includes an RF transmitter for producing a wide variety of RFpulses used in MR pulse sequences. The RF transmitter is responsive tothe scan prescription and direction from the pulse sequence server 110to produce RF pulses of the desired frequency, phase, and pulseamplitude waveform. The generated RF pulses may be applied to the wholebody RF coil 128 or to one or more local coils or coil arrays.

The RF system 120 also includes one or more RF receiver channels. EachRF receiver channel includes an RF preamplifier that amplifies the MRsignal received by the coil 128 to which it is connected, and a detectorthat detects and digitizes the quadrature components of the received MRsignal. The magnitude of the received MR signal may thus be determinedat any sampled point by the square root of the sum of the squares of theI and Q components:

M=√ I ² +Q ²   (1),

and the phase of the received MR signal may also be determined:

$\begin{matrix}{\phi = {{\tan^{- 1}\left( \frac{Q}{I} \right)}.}} & (2)\end{matrix}$

The pulse sequence server 110 also optionally receives patient data froma physiological acquisition controller 130. The controller 130 receivessignals from a number of different sensors connected to the subject tobe scanned, such as electrocardiograph (ECG) signals from electrodes, orrespiratory signals from a bellows or other respiratory monitoringdevice. Such signals are typically used by the pulse sequence server 110to synchronize, or “gate,” the performance of the scan with thesubject's heart beat or respiration.

The pulse sequence server 110 also connects to a scan room interfacecircuit 132 that receives signals from various sensors associated withthe condition of the patient and the magnet system. A patientpositioning system 132 may be included.

The digitized MR signal samples produced by the RF system 120 arereceived by the data acquisition server 112. The data acquisition server112 operates in response to instructions downloaded from the workstation102 to receive the real-time MR data and provide buffer storage, suchthat no data is lost by data overrun. In some scans, the dataacquisition server 112 does little more than pass the acquired MR datato the data processor server 114. However, in scans that requireinformation derived from acquired MR data to control the furtherperformance of the scan, the data acquisition server 112 is programmedto produce such information and convey it to the pulse sequence server110. For example, during prescans, MR data is acquired and used tocalibrate the pulse sequence performed by the pulse sequence server 110.Also, navigator signals may be acquired during a scan and used to adjustthe operating parameters of the RF system 120 or the gradient system118, or to control the view order in which k-space is sampled. In allthese examples, the data acquisition server 112 acquires MR data andprocesses it in real-time to produce information that is used to controlthe scan.

The data processing server 114 receives MR data from the dataacquisition server 112 and processes it in accordance with instructionsdownloaded from the workstation 102. Such processing may include, forexample: Fourier transformation of raw k-space MR data to produce two orthree-dimensional images; the application of filters to a reconstructedimage; the performance of a backprojection image reconstruction ofacquired MR data; the generation of functional MR images; and thecalculation of motion or flow images.

Images reconstructed by the data processing server 114 are conveyed backto the workstation 102 where they are stored. Real-time images arestored in a data base memory cache (not shown), from which they may beoutput to operator display 104 or a display 136 that is located near themagnet assembly 124 for use by attending physicians. Batch mode imagesor selected real time images are stored in a host database on discstorage 138. When such images have been reconstructed and transferred tostorage, the data processing server 114 notifies the data store server116 on the workstation 102. The workstation 102 may be used by anoperator to archive the images, produce films, or send the images via anetwork or communication system 140 to other facilities that may includeother networked workstations 142.

The communication system 140 and networked workstation 142 may representany of the variety of local and remote computer systems that may beincluded within a given imaging facility including the system 100 orother, remote location that can communicate with the system 100. In thisregard, the networked workstation 142 may be functionally and capablysimilar or equivalent to the operator workstation 102, despite beinglocated remotely and communicating over the communication system 140. Assuch, the networked workstation 142 may have a display 144 and akeyboard 146. The networked workstation 142 includes a processor 148that is commercially available to run a commercially-available operatingsystem. The networked workstation 142 may be able to provide theoperator interface that enables scan prescriptions to be entered intothe MRI system 100.

Referring to FIG. 2A, a variation on the MRF acquisition is illustratedthat provides a priori variation in both FA and TR to generate T₁ and T₂specific MRF signal evolution profiles. The FISP-MRF acquisition shownherein is only one example of a dynamic MRF acquisition and isdistinguished from basic gradient echo sequences, by the inclusion ofspoiler gradients 190 and rewinder gradients 192, for example, followingsignal readout 194. The FISP-MRF acquisition may be desirable instead ofTrue FISP or echo planar imaging (EPI) pulse sequence to limit “banding”artifacts, which are especially problematic on high field MRI scannersdue to increased inhomogeneity in the magnetic field. The FISP-MRFacquisition also provides increased T₂ sensitivity in comparison withother incoherent steady-state MRI techniques (ex. Fast Low Angle Shot,FLASH). However, FIG. 2A is not a traditional FISP pulse sequence.Rather, the pulse sequence of the present disclosure provides, as willbe described, provides for dynamic flip angle and TR variation profilesthat are designed to increase the sensitivity of the signal evolutionprofiles to the MR-sepcific parameters (i.e., T₁ and T₂).

In particular, the MRF acquisition is initiated with one or morepreparation modules 200. As illustrated, the preparation module(s) 200may include an inversion pulse 201 to enhance the overall T₁sensitivity. However, in other configurations, the preparation module200 may include a magnetization preparation scheme to sensitize the MRFacquisition to another desired parameter(s) of interest.

The preparation module(s) 200 is/are followed by multiple (as anon-limiting example, 600) successive FISP acquisition modules 202a-202′. As illustrated, the excitation FA (in degrees) 204 of each FISPacquisition module 202 a-202′ varies, as does the TR (in ms) 206.Non-limiting examples of FA and TR variation profiles are shown in FIGS.2B and 2C, respectively. In the illustrated, non-limiting example, theecho time was held constant (TE=3.2 ms). FIG. 2B shows that a repeatingsinusoidal FA pattern may be used that ranges from 0 to 70 degrees.Additional FA lobes were used compared to original clinical MRFtechniques to provide additional image contrast. As shown in FIG. 2C,the non-limiting example TR pattern selected was a Perlin noise pattern,similar to the original clinical MRF description. However, a higherrange of TR values was in the illustrated non-limiting example (i.e.,12.0 ms to 25.3 ms) to obtain a reasonable signal-to-noise ratio (SNR)for the MRF images (e.g., 600 images).

Referring to FIG. 3, a process 300 for acquiring MRF data at high fieldstrength (e.g., above 4.7 T) begins at process block 302 by positioninga subject within a bore of a magnetic resonance system. The system maybe that of a clinically adapted scanner that is capable of operatingabove 4.7 T, including 7 T and above, or may be a preclinical scanner oranimal scanner, such as a Bruker Biospec 7 T MRI scanner (Billerica,Mass.) equipped with a 400 mT/m magnetic field gradient insert.

To prepare for data acquisition, as described above, an inversion pulsemodule is performed at process block 304. Then, at process block 306 anacquisition module is performed. As described above, a series ofacquisition modules are performed. Thus, at process block 308, a checkis made to determine if the preceding module was the last desiredmodule. If not, the FA and/or TR is varied at process block 310 and thenext acquisition module is performed with the varied FA and/or TR. Inparticular, to acquire the MRF signal evolutions, the MRF acquisition ofFIG. 2A was designed to acquire the same line of k-space for each of thesequential images during each MRF-scan repetition period (e.g., 11.6seconds). The FISP-MRF kernel shown herein was designed with a filtered7-lobe sinc radiofrequency (RF) excitation pulse (T_(RF)=2 ms,bandwidth=7000 Hz) to ensure a uniform excitation slice profile withlimited excitation sidebands. Although not required, a 5-second delaywas inserted after each MRF scan repetition (MRF-FISP TRs) to allow themagnetization to return to equilibrium prior to the next MRF scanrepetition and to limit the duty cycle on the magnetic field gradients.This process was repeated for each line of k-space to generate, in thisnon-limiting example, 600 total temporal MRF images.

Once all the desired acquisition modules are completed, the acquireddata can be reconstructed. The MRF reconstruction process utilizes alarge dictionary of signal evolution profiles that are subsequently“matched” to the acquired MRF signal evolution profile for each imagingvoxel using vector-based inner product comparisons. The MRF dictionarycan be created, as described previously, using Bloch equationsimulations of the MRF acquisition. However, an MRF dictionary for highfield preclinical MRI scanners can be designed to reflect increased T₁and decreased T₂ values observed on high field MRI scanners.

The MRF dictionary can include tens of thousands of profiles generatedfrom many T₁ values (100 to 2000 ms, increment=10 ms; 2000 to 6000 ms,increment=500 ms) and T₂ values (10 to 150 ms, increment=2 ms; 150 to300 ms, increment=5 ms) or any other MR parameter (e.g., perfusion,diffusion, chemical exchange saturation transfer). Note that the T₁ andT₂ increments in the MRF dictionary (2-10 ms for low T₁/T₂ values) aresmall relative to the expected variation observed for in vivo T₁/T₂estimates. With reconstruction complete, a report can be generated atprocess block 312. The report can include visual reports (e.g., imagesor maps), text or metric based reports, audio reports, and the like.

Experiments

For example, to perform an in vitro MRF assessments of T₁ and T₂relaxation times and proton density (M₀), four imaging phantoms withdistinctly different T₁ and T₂ relaxation times were prepared by addingdifferent concentrations of MnCl₂ (30, 100, 200, 300 μM, respectively)to distilled water. The solutions were added to 200 μL centrifuge tubesfor imaging. Axial MRF images (600 total MRF images) were obtained forthese phantoms. Additional MRF acquisition parameters for the phantomexperiments were: FOV=3 cm×3 cm, matrix=128×128, slice thickness=1.5 mm,total acquisition time=35 min 24 s. Although larger than typical, aslice thickness of 1.5 mm was used for this initial preclinical MRFdevelopment to limit the effects of noise. Conventionalinversion-recovery spin echo (IR-SE) (inversion delay times=[50, 200,350, 500, 650, 800, 1000, 1500, 2000, 2500, 3000, 4000, 5000, 8000,10,000] ms, TR/TE=10,000 ms/8.1 ms, one average, total acquisitiontime=6.7 h) and spin echo (SE) (echo times=[10, 25, 40, 60, 90, 120,150, 300, 400, 500, 800] ms, TR=10,000 ms, one average, totalacquisition time=3.9 h) methods were also implemented to generateconventional estimates of the phantom T₁, T₂, and M₀ values,respectively for comparison with the MRF findings. All other imagingparameters including field of view and resolution were identical for theMRF, IR-SE (T₁), and SE (T₂, M₀) acquisitions. Quantitative T₁ and T₂relaxation times and proton density (M₀) maps were obtained from the MRFdata. T₁ and T₂ relaxation time and M₀ maps were calculated for theconventional IR-SE and SE acquisitions using established methods.

The MRF and conventional acquisitions were repeated five times ondifferent days to assess the reproducibility of the MRF estimates. MeanT₁, T₂, and M₀ values for all methods were obtained for each phantomusing a region of interest (ROI) analysis. In addition, voxel-wise errormaps and standard deviations for T₁, T₂, and M₀ were calculated toassess voxel-wise variation. Two-tailed Student's t-tests were used tocompare the mean and voxel-wise variation in the MRF T₁, T₂, and M₀values with the IR-SE and SE data, respectively.

Initial in vivo and ex vivo kidney MRF assessments were performed in ahealthy mouse. In vivo and ex vivo MRF images were acquired for thekidneys of a 12-month old healthy mouse (C57/BL6, Jackson Labs) toassess the impact of respiratory motion. The mouse was initiallyanesthetized in 1-2% isoflurane with supplemental oxygen and positionedin a mouse volume coil for imaging (inner diameter=35 mm). The bodytemperature and breathing rate of the animal were maintained at 35±1° C.and 40-60 breaths/min with adjustable warm air and isoflurane levels,respectively. Single-slice, axial kidney MRF images were obtained forthe mouse (FOV=3 cm×3 cm, matrix=128×128, slice thickness=1.5 mm).Notably, no respiratory triggering was applied for these MRFacquisitions to demonstrate the robustness of the MRF technique torespiratory motion artifacts. Immediately following the in vivo MRFscans, the isoflurane concentration was increased to 5% for 30 minutesto euthanize the mouse within the MRI scanner with no repositioning. TheMRF acquisition was then repeated on the same axial kidney imaging sliceto generate an ex vivo MRF dataset with no respiratory motion artifacts.All MRF acquisition and reconstruction parameters for the in vivo and exvivo kidney experiments were the same.

Further still, initial MRF assessments in a mouse brain tumor model werealso performed. In vivo and ex vivo MRF assessments were also performedon mouse brains orthotopically implanted with a human glioma cell lineexpressing green fluorescent protein (GFP) to demonstrate thesensitivity of the MRF technique to known pathology and to examine theeffects of RF excitation slice profile on the MRF estimates of T₁, T₂,and M₀. To prepare the mouse glioma model, Gli36Δ5 cells were infectedwith GFP encoding lentivirus, harvested for intracranial implantation bytrypsinization, and concentrated to 1×10⁵ cells/μL in PBS. A six-weekold female athymic nude mouse was anesthetized by intraperitonealadministration of 50 mg/kg ketamine/xylazine and fitted into astereotaxic rodent frame (David Kopf Instruments, Tujunga, Calif.).Tumor cells were implanted at AP=+0.5 and ML=−2.0 from the bregma in theright striatum at a depth of 3 mm below the dura using a 10-4 syringe(26-gauge needle; Hamilton Co, Reno, Nev.). A total of 200,000 gliomacells were implanted.

In vivo MRF scans were obtained 8 days following inoculation with thetumor cells. As for the kidney MRF studies, the animal was anesthetizedwith 1-2% isoflurane and positioned within a 35-mm inner diameter volumecoil within the 7 T Bruker Biospec MRI scanner. Conventional T₂-weightedimages of the glioma model were obtained from a Rapid Acquisition withRelaxation Enhancement (RARE) acquisition (TR/TE=3000 ms/40.0 ms,FOV=3×3 cm, matrix=256×256, slice thickness=1.5 mm, three averages,total acquisition time=8 min) to identify the tumor region. The MRFparameters for the in vivo brain tumor assessments were (FOV=3 cm×3 cm,matrix=128×128, slice thickness=1.5 mm, excitation sinc7 pulse, totalacquisition time=35 min 24 s). The in vivo brain MRF data were used togenerate T₁, T₂, and M₀ maps reconstructed from the full MRF dataset(600 images) as well as subsets of 100, 300, and 500, imagesrespectively to determine the number of MRF images needed for effectivequantification. Mean brain T₁, T₂, and M₀ values were obtained from anROI analysis of each set of MRF reconstructions for comparison.

Ex vivo MRF scans of a separate excised mouse brain tumor model wereacquired to obtain an initial investigation into the impact ofexcitation slice profile on MRF estimates. The mouse glioma model wasprepared as described above. After 10 days of tumor growth, the animalwas sacrificed, and the brain was excised for fluorescence imaging usinga Maestro FLEX fluorescence scanner (CRi, Hopkinton, Mass.) to verifytumor viability. Fluorescence images of the GFP-expressing Gli36Δ5 tumorcells were acquired using standard GFP filters (excitation=445-490 nm,emission=515 nm long-pass filter, acquisition settings=500-720 in 10-nmsteps). Brightfield images were also acquired to provide an anatomicroadmap. Fluorescence and brightfield exposure durations were 10milliseconds and 300 milliseconds, respectively. Fluorescence imageswere background subtracted and unmixed, using the Maestro software tospectrally separate brain auto-fluorescence from GFP-expressing tumorcells. The brain was then placed in neutral buffered 10% formalin(Sigma-Aldrich, Milwaukee, Wis., USA) within a 15 mL centrifuge tube forex vivo MRF imaging.

The MRF acquisition (FOV=3 cm×3 cm, matrix=128×128, slice thickness=1.5mm, total acquisition time=35 min 24 s) was repeated using either afiltered 7-lobe sinc radiofrequency (RF) excitation pulse (T_(RF)=2 ms)or a hermite RF excitation pulse (T_(RF)=2 ms). The slice profiles foreach RF excitation pulse were measured using a conventional Fast LowAngle SHot (FLASH) acquisition (FOV=1.52 cm×1.52 cm, matrix=256×256,TR/TE=100.0 ms/10.0 ms, FA=10 degrees) by applying the readout gradientalong the axial slice-select direction. Although the sinc7 pulse wouldbe expected to provide a more uniform excitation with reduced sidebands,these two pulses were selected as they are commonly used for manypreclinical MRI acquisitions. Following the ex vivo MRF acquisition, themouse brain was sectioned at 8 μm per section and stained withhematoxylin and eosin (H&E) stains to further validate the existence ofthe brain tumor by histology.

Results

Example in vitro phantom images of MnCl₂-doped phantoms at multiple MRFtimepoints (image numbers 40, 80, and 150 out of 600 total) andsingle-voxel signal evolution profiles the varying contrast in thephantom images at three selected time points (40, 80, 150). ResultantMRF-based T₁, T₂, and M₀ maps for the four MnCl₂-doped water phantomsare were compared to corresponding T₁, T₂, M₀ maps acquired usingconventional inversion recovery spin echo (IR-SE) and spin echo (SE)pulse sequence.

A comparison of the performance of SE measures with the MRF resultsacquired using the systems and methods of the present disclosure isillustrated in FIGS. 4A and 4B, which provide graphs of mean phantom T₁values and T₂ values, respectively. Quantitative evaluation of these invitro MRF against conventional maps showed similar overall results inthe MRF data, based on mean (±standard deviations) T₁, T₂ and M₀ valuesfor the MRF from an ROI analysis of the five repeat scans. The MRF-basedT₁ estimates for all four phantoms were not statistically different fromthe conventional IR-SE T₁ maps (p>0.1), while the mean T₂ and M₀ MRFestimates exhibited some significant differences. The mean T₂ estimatesfrom the MRF scans were significantly higher than from the conventionalSE methods for three of the four phantoms (p<0.01) while the phantomwith the highest T₂ value was significantly lower than the estimatesfrom the conventional SE methods (p<0.01). Two of the four phantoms hadsignificant higher M₀ values from MRF scans compared to SE methods(p<0.05). Despite these differences, the MRF results showed significantdecreases in both T₁ and T₂ values for increasing MnCl₂ concentrationsas expected (p<0.01).

Axial MRF-based T₁ and T₂ relaxation time and M₀ maps from healthy mousekidneys and brain showed hyperintense regions in the ventricles of themouse brain and renal medulla, as reflected in the T₁ and T₂ maps.Importantly, the in vivo kidney T₁, T₂, and M₀ maps were devoid ofrespiratory motion artifacts typical of conventional in vivo preclinicalscans. Only minor ghosting artifacts are visible in the in vivo kidneyT₂ maps due to aortic pulsatility. In addition, the in vivo and ex vivokidney maps are very similar with only minor decreases in the ex vivo T₁and T₂ values likely due to the absence of flowing blood. As furtherverification, MRF signal profiles and respective “matched” dictionaryprofiles from an ROI of the right in vivo and ex vivo kidney wereanalyzed.

Specifically, FIG. 5 provides a graph of signal intensity versus numberof MRF images (0-600) that were acquired. The graph shows both theacquired signal 500 for each image, and the dictionary values 500. Ascan be seen, the acquired signal 500 and dictionary signal 502 arenearly matched, save for a plurality of respiratory motion spikes 504.Thus, as illustrated in FIG. 5, a similar shape of the acquired MRFprofiles, aside from the respiratory motion “spikes” for the in vivo MRFscan, were observed. Importantly, the matched MRF profile for the invivo scan did not include these respiration spikes as the motion is notencoded in any of the signal evolution profiles in the MRF dictionary.

In vivo MRF-based T₁, T₂, and M₀ maps of a fixed ex vivo mouse brainorthotopically implanted with a human glioma cell line were produced.Corresponding T₂-weighted anatomic MRI images, combined fluorescence andbrightfield images of the GFP expressing glioma cells, and H&E stainvalidating tumor presence and location were also produced. An ROIanalysis showed mean T₁, T₂ and M₀ values in the glioma (T₁=1973 ms,T₂=82 ms, M₀=0.5 ms), cerebral cortex (T₁=1470 ms, T₂=63 ms, M₀=0.4) andthalamus (T₁=1273 ms, T₂=55 ms, M₀=0.4 ms) that are comparable toresults in previous studies. No ghosting artifacts were observed inthese in vivo MRF T₂ maps of a mouse brain.

The T₁, T₂, and M₀ maps were reconstructed from subsets of the same invivo MRF data using the first 100, 300, 500, and 600 images in the MRFprofile, respectively. The MRF maps obtained from reconstruction of thefull dataset (N=600) were included as a reference. Mean brain T₁, T₂, M₀values using the first 600, 500, and 300 images were consistent (rangeof mean T₁=1436 to 1461 ms; range of mean T₂=65-69 ms; range of meanM₀=0.4-0.5). However, the mean values were substantially different usingonly the first 100 MRF images in the matching process (T₁=1648 ms, T₂=52ms, M₀=0.8). While not a rigorous evaluation, these findings illustratethe value of acquiring a sufficient number of MRF images as well asopportunities for optimizations to reduce the overall MRF acquisitiontime.

MRF T₁, T₂, and M₀ maps were also generated from an ex vivo mouse brainwith an implanted glioma using either a sinc7 or hermite RF excitationpulses in the MRF-FISP acquisition. The magnitude of the RF excitationpulse shape and the corresponding measured slice profiles were obtainedfrom the conventional FLASH acquisition. The sinc7 excitation pulseprovided a more uniform tip angle across the entire imaging slice andreduced side-band excitations in comparison to the hermite excitationpulse, as expected. The MRF T₁ and T₂ estimates using the sinc7 RF pulse(T₁=970 ms, T₂=59 ms, M₀=0.8) were lower relative to the correspondingvalues obtained with the hermite pulse (T₁=1078 ms, T₂=146 ms, M₀=0.7),while the M₀ values were slightly lower. Overall, the T₂ estimatesappear to be more sensitive to excitation profile as the estimatesobtained from the sinc7 pulse (T₂=59 ms) were approximately one halfthat obtained from the hermite pulse (T₂=146 ms).

The above-described systems and methods for extending MRF techniques tohigh-field, preclinical MR systems builds upon the previously-reportedclinical MRF implementation and combines a priori flip angle andrepetition time variation profiles with an adaptable preparation moduleor modules (e.g., inversion preparation) with a dynamic MR acquisitionkernel to obtain MRF signal evolution profiles that can quantifyMR-specific parameters (e.g., T₁ and T₂ relaxation times). Thehigh-field, preclinical MRF techniques provided herein can be used toprovide quantitative in vivo assessments of T₁, T₂, and M₀ (and/orvirtually any other MR parameter) with resistance to respiratory motionartifacts. In addition, the simultaneous estimation of multiple imagingparameters provides the opportunity to identify and quantify tissuepathophysiology without the physiologic confounds of conventionalmethods that require sequential parameter estimation. As such, thishigh-field MRF technique provides a uniquely powerful imaging platformthat can be used to assess normal anatomy and physiology as well asdisease pathophysiology and therapeutic efficacy in a wide variety ofdisease models.

By incorporating flip angle and repetition time variation profiles intoa dynamic MR imaging acquisition with an initial inversion preparation,a system and method is provided to obtain signal evolution profiles thatare sensitive to T₁, and T₂ relaxation times (or other MR parameters).This new high-field, preclinical MRF technique provides simultaneousestimation of multiple imaging parameters with insensitivity torespiratory motion artifacts typical of previously reported MRItechniques.

The MRF acquisition can be designed with a variety of dynamic MRacquisitions. By using a FISP imaging kernel, instead of True FISP orEPI imaging kernels, “banding” and distortion/ghosting artifacts arecontrolled in the dynamic MRF images. Importantly, these artifacts areespecially problematic on high field MRI scanners due to increased B₀inhomogeneities. The FISP-MRF acquisition also provides increased T₂sensitivity in comparison with other incoherent steady-state MRItechniques, such as FLASH acquisitions. The MRF FA and TR variationprofiles illustrated in FIGS. 2B and 2C can also be modified to increasethe sensitivity of the signal evolution profiles to the desired MRparameters to be estimated. For example, the sinusoidal FA profiles canbe expanded to provide sufficient weighting over the many MRF images. Inaddition, the Perlin-noise TR variation profile can be used, albeit withan expanded TR range to increase the SNR of the MRF images. Thepreclinical MRF dictionary may be adjusted to reflect the desired MRparameters to be estimated on high field, preclinical MRI scanners.

The example in vitro MRF results shown here demonstrate that the MRFmethodology described herein is comparable to conventional quantitativeassessments and, in various settings, superior. The in vitro T₁ and M₀values are similar for both the MRF and conventional measures andresulted in significantly different mean T₁ values for the differentMnCl₂-doped phantoms as expected (p<0.0001). As described, the MRF-basedestimate at high field, preclinical MR scanners is significantly moreuniform than conventional measures. These results are consistent withthe original clinical MRF report comparing MRF with DESPOT1 and DESPOT2.While the MRF-based T₂ estimates also differentiated the in vitrophantoms (p<0.01), the MRF-estimates were also significantly differentthan the estimates from the conventional spin echo methods for all fourphantoms. Interestingly, the MRF-based T₂ estimate were all lower thanthe conventional methods except for the phantom with the highest T₂value. In addition, the MRF T₂ estimates resulted in increasedvoxel-wise variation. Other dynamic MR acquisition kernels may beimplemented within this MRF acquisition (e.g., True FISP) to provideimproved T₂ accuracy and/or sensitivity to other MR parameters.

Small B₁ heterogeneity errors can have an effect on the T₂ estimates. Inswitching from sinc7 to hermite RF excitation pulses, the MRF T₂estimates for the in vitro mouse brain increase from ˜60 ms (sinc7) to˜140 ms (hermite). At the same time, the T₁ and M₀ values also change,but to a much smaller degree. Therefore, it can be desirable toincorporate B₁ corrections into the MRF dictionary/reconstructionprocess in order to improve both the accuracy as well as the uniformityof the T₂ estimates. These imperfections in slice profile/tip angle,along with high noise levels, may result in imperfect matches betweenthe acquired and theoretical signal evolution profiles. Astraightforward method to correct for B₁ inhomogeneities is to measurethe B₁ field directly, either before or after the MRF data acquisition.Alternatively, the B1 field variation may be directly estimated withinthe MRF dictionary.

As mentioned above, the high field MRF technique described herein has aninherent resistance to bulk motion artifacts. This resistance to motionartifacts is an advantageous feature for all in vivo preclinical imagingapplications because it allows for free breathing (i.e., a lack ofbreathholding) and provides the opportunity to avoid respiratory motionartifacts and/or respiratory gating. The underlying cause of theinherent motion insensitivity of the MRF technique is established bymaintaining respiration rates of typically 40-60 breaths/minute, withlittle or no respiratory motion observed for a majority of the timebetween breaths. As further illustrated in FIG. 5, this ˜1 secondquiescent period allows up to 100 motion-free MRF data points to beacquired between the ˜100 ms respiration “spikes” 502 as shown in FIG.5. At the same time, the respiratory motion “spikes” were not includedin the theoretical MRF dictionary profiles. Therefore, advantageously,the spikes were ignored in the MRF matching process to generateparameter estimates for each voxel with minimal impact of respiratorymotion. An alternative to the approach herein is to use multiplenavigator pulses to trigger the dynamic MRF acquisition.

The above-described, high-field MRF technique, as described herein, canbe extended to non-Cartesian trajectories (e.g. radial, spiral) toreduce the overall acquisition time to acquire the MRF image sets. Forexample, utilization of a single-shot spiral trajectory, can be used toreduce the overall acquisition time to as little as 10-20 seconds foreach imaging slice. Also, Parallel imaging and/or compressed sensingstrategies can be incorporated to enable reductions in both Cartesianand non-Cartesian MRF trajectories. Further, the number of MRF imagesacquired in the above-described experiments can be reduced, for example,halved to 300 images, with no obvious limited impact on T₁ and T₂quantification. These reductions in MRF acquisition time can then beutilized to develop effective multi-slice 2D or 3D MRF acquisitions.

The above-described, high-field MRF technique can be used to quantifyother MRI parameters besides those described. While this initialimplementation focused primarily on estimation of T₁ and T₂ relaxationtimes and proton density, the MRF acquisition can be modified tosimultaneously estimate perfusion and diffusion parameters, which areimportant in non-invasively assessing ischemic diseases such as stroke,acute kidney injury, and cardiac injury models. This MRF methodology canbe used to assess virtually any MR parameter by tailoring themagnetization preparation module(s) and/or the dynamic MR acqusitionmodules.

The above-described, high-field MRF technique can be further adapted toperform an assessment of chemical exchange saturation transfer (CEST)and magnetization transfer (MT) contrast mechanisms to assessintracellular pH, ATP depletion rates, cartilage composition, andglucose/glycogen metabolism, for example. These CEST/MT methods require5-50 sequential acquisitions to generate quantitative assessmentsresulting in extended acquisition times. Adapting the MRF acquisition toassess these parameters may incorporate specific magnetizationpreparation periods (ex. an off-resonance CEST preparation pulse schema)at one or more points in the MRF acquisition. Similar to the inversionpreparation design used here, this magnetization preparation scheme isused to sensitize the MRF acquisition to the desired parameters ofinterest. A different MRF dictionary with a range of values for thespecific parameters is then used.

In conclusion, a high-field MRF technique is provided that may be usedfor a variety of applications, including high field, preclinical MRIscanners. An MRF acquisition is presented that limits motion artifactson high field, preclinical MR scanners to generate both T₁ and T₂sensitivity in the dynamic MRF acquisition. In combination with the MRFdictionary, the example MRF-FISP acquisition presented herein resultedin simultaneous T₁, T₂, and proton density maps of both phantoms andmouse brain and kidneys in a single acquisition. The MRF methodology hasdemonstrated inherent resistance to respiratory motion artifacts inmouse abdominal imaging applications. This is a significant improvementover conventional MRI techniques relying on either breath holding orrespiratory gating which are either not possible or are frequentlyunreliable in preclinical imaging applications. Initial in vivo mousebrain tumor studies also demonstrate sensitivity to known pathology.This MRF technique can also be adapted to assess other MRI parametersincluding diffusion and perfusion establishing a platform of MRItechnology to enable multi-parametric imaging assessments in rodentmodels to assess mechanisms of disease initiation and progression aswell as the efficacy of novel therapies.

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, and modifications, aside fromthose expressly stated, are possible and within the scope of theinvention.

1. A method for operating a high-field magnetic resonance (MR) system toacquire quantitative data from a subject arranged with in the MR system,wherein the method includes steps comprising: controlling the high-fieldMR system to perform a preparation module configured to perform one ofan inversion pulse or a magnetization preparation; controlling thehigh-field MR system to, following the preparation module, perform aseries of data acquisition modules without respiratory gating, whereineach data acquisition module is formed of a steady-state free precessionpulse sequence and wherein performing the series of data acquisitionmodules includes varying at least one of an amplitude of an excitationpulse or a repetition time of the steady-state free precession pulsesequence between adjacent data acquisition modules in the series of dataacquisition modules to acquire a series of MR data with random orpseudo-random imaging acquisition parameters; comparing the series of MRdata to a dictionary of signal evolution profiles to determine a matchbetween the series of MR data with at least one signal evolution profilein the dictionary; and generating a report indicating at least onequantitative parameter in the subject based on the MR data and the matchto the at least one signal evolution profile in the dictionary.
 2. Themethod of claim 1 wherein the dictionary of signal evolution profiles isfree of respiratory spikes.
 3. The method of claim 1 wherein varying atleast one of an amplitude of an excitation pulse or a repetition time ofthe steady-state free precession pulse sequence includes performing asinusoidal FA profile or a Perlin-noise TR variation profile.
 4. Themethod of claim 1 wherein the high-field MR system includes a staticmagnetic field of at least 4.7 Tesla.
 5. The method of claim 1 whereinthe signal evolution profiles are sensitive to T₁ or T₂ relaxationtimes, or proton density signals.
 6. The method of claim 1 wherein thesteady-state free precession pulse sequence implements a non-Cartesiansampling pattern.
 7. The method of claim 1 wherein MR data includes oneof chemical exchange saturation transfer (CEST), magnetization transfer(MT), diffusion, or perfusion weighted data.
 8. A magnetic resonance(MR) system, comprising: a magnet system configured to generate a staticmagnetic field of at least 4.7 Tesla about at least a portion of asubject arranged in the MR system; a gradient system configured toestablish at least one magnetic gradient field with respect to thestatic magnetic field; a radio frequency (RF) system configured todeliver excitation pulses to the subject and acquire imaging data fromthe subject; a computer system programmed to: control the gradientsystem and RF system to perform a preparation module including one of aninversion pulse or a magnetization preparation; control the gradientsystem and RF system to perform a series of data acquisition modules,wherein each data acquisition module is formed of a steady-state freeprecession pulse sequence and wherein performing the series of dataacquisition modules includes varying at least one of an amplitude of anexcitation pulse or a repetition time of the steady-state freeprecession pulse sequence between adjacent data acquisition modules inthe series of data acquisition modules to acquire a series of MR dataincluding respiratory spikes; and compare the series of MR data to adictionary of signal evolution profiles to determine a match between theseries of MR data with at least one signal evolution profile in thedictionary; and based on the match, determine at least one quantitativeparameter of the subject.
 9. The MR system of claim 8 wherein thecomputer system is further programmed to determine the match bycomparing the MR data with respiratory spikes to signal evolutionprofiles without respiratory spikes.
 10. The MR system of claim 8wherein the computer system is further programmed to acquire the MR dataduring quiescent periods between respiratory motion of the subject. 11.The MR system of claim 8 wherein the computer system is furtherprogrammed to vary the at least one of the amplitude of the excitationpulse or the repetition time of the steady-state free precession pulsesequence by performing a sinusoidal FA profile or a Perlin-noise TRvariation profile.
 12. The MR system of claim 8 wherein the signalevolution profiles are sensitive to T₁ or T₂ relaxation times, or protondensity signals.
 13. The MR system of claim 8 wherein the computersystem is further programmed to acquire the MR data using anon-Cartesian sampling pattern.
 14. The MR system of claim 8 wherein MRdata includes one of chemical exchange saturation transfer (CEST),magnetization transfer (MT), diffusion, or perfusion weighted data. 15.The MR system of claim 8 wherein the computer system is furtherconfigured to generate a report indicating the at least one quantitativeparameter of the subject.
 16. The MR system of claim 8 wherein thesteady-state free precession pulse sequence includes a fast imaging withsteady-state free precession (FISP) pulse sequence.
 17. A method foroperating a high-field magnetic resonance (MR) system to perform amagnetic resonance fingerprinting process, wherein the method includessteps comprising: controlling a high-field MR system with a staticmagnetic field of at least 4.7 Tesla to perform a preparation moduleconfigured to perform one of an inversion pulse or a magnetizationpreparation; controlling the high-field MR system to, following thepreparation module, perform a series of data acquisition modules withoutrespiratory gating, wherein each data acquisition module is formed of asteady-state free precession pulse sequence and wherein performing theseries of data acquisition modules includes imaging parameters toacquire a series of MR data; performing a magnetic resonancefingerprinting reconstruction process by comparing the series of MR datato a dictionary to generate a report indicating at least onequantitative parameter in the subject.
 18. The method of claim 17wherein the dictionary is consists of free-breathing, MR signalevolutions.
 19. The method of claim 17 wherein the series of MR data isacquired during quiescent periods between respiratory motion of thesubject.
 20. The method of claim 17 further comprising generating areport indicating the at least one quantitative parameter in the subjectmapped onto an anatomical image of the subject.